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	# Neural Machine Translation

	This README contains instructions for [using pretrained translation models](#example-usage-torchhub)
	as well as [training new models](#training-a-new-model).

	## Pre-trained models

	Model \| Description \| Dataset \| Download
	---\|---\|---\|---
	`conv.wmt14.en-fr` \| Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) \| [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) \| model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
	`conv.wmt14.en-de` \| Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) \| [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) \| model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2)
	`conv.wmt17.en-de` \| Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) \| [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) \| model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2)
	`transformer.wmt14.en-fr` \| Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) \| [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) \| model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-fr.joined-dict.newstest2014.tar.bz2)
	`transformer.wmt16.en-de` \| Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) \| [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) \| model: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2) <br> newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
	`transformer.wmt18.en-de` \| Transformer <br> ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381)) <br> WMT'18 winner \| [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) \| model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) <br> See NOTE in the archive
	`transformer.wmt19.en-de` \| Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner \| [WMT'19 English-German](http://www.statmt.org/wmt19/translation-task.html) \| model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz)
	`transformer.wmt19.de-en` \| Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner \| [WMT'19 German-English](http://www.statmt.org/wmt19/translation-task.html) \| model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz)
	`transformer.wmt19.en-ru` \| Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner \| [WMT'19 English-Russian](http://www.statmt.org/wmt19/translation-task.html) \| model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz)
	`transformer.wmt19.ru-en` \| Transformer <br> ([Ng et al., 2019](https://arxiv.org/abs/1907.06616)) <br> WMT'19 winner \| [WMT'19 Russian-English](http://www.statmt.org/wmt19/translation-task.html) \| model: <br> [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz)

	## Example usage (torch.hub)

	We require a few additional Python dependencies for preprocessing:
	```bash
	pip install fastBPE sacremoses subword_nmt
	```

	Interactive translation via PyTorch Hub:
	```python
	import torch

	# List available models
	torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt16.en-de', ... ]

	# Load a transformer trained on WMT'16 En-De
	# Note: WMT'19 models use fastBPE instead of subword_nmt, see instructions below
	en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de',
	tokenizer='moses', bpe='subword_nmt')
	en2de.eval() # disable dropout

	# The underlying model is available under the models attribute
	assert isinstance(en2de.models[0], fairseq.models.transformer.TransformerModel)

	# Move model to GPU for faster translation
	en2de.cuda()

	# Translate a sentence
	en2de.translate('Hello world!')
	# 'Hallo Welt!'

	# Batched translation
	en2de.translate(['Hello world!', 'The cat sat on the mat.'])
	# ['Hallo Welt!', 'Die Katze saß auf der Matte.']
	```

	Loading custom models:
	```python
	from fairseq.models.transformer import TransformerModel
	zh2en = TransformerModel.from_pretrained(
	'/path/to/checkpoints',
	checkpoint_file='checkpoint_best.pt',
	data_name_or_path='data-bin/wmt17_zh_en_full',
	bpe='subword_nmt',
	bpe_codes='data-bin/wmt17_zh_en_full/zh.code'
	)
	zh2en.translate('你好世界')
	# 'Hello World'
	```

	If you are using a `transformer.wmt19` models, you will need to set the `bpe`
	argument to `'fastbpe'` and (optionally) load the 4-model ensemble:
	```python
	en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de',
	checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
	tokenizer='moses', bpe='fastbpe')
	en2de.eval() # disable dropout
	```

	## Example usage (CLI tools)

	Generation with the binarized test sets can be run in batch mode as follows, e.g. for WMT 2014 English-French on a GTX-1080ti:
	```bash
	mkdir -p data-bin
	curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 \| tar xvjf - -C data-bin
	curl https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2 \| tar xvjf - -C data-bin
	fairseq-generate data-bin/wmt14.en-fr.newstest2014 \
	--path data-bin/wmt14.en-fr.fconv-py/model.pt \
	--beam 5 --batch-size 128 --remove-bpe \| tee /tmp/gen.out
	# ...
	# \| Translated 3003 sentences (96311 tokens) in 166.0s (580.04 tokens/s)
	# \| Generate test with beam=5: BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)

	# Compute BLEU score
	grep ^H /tmp/gen.out \| cut -f3- > /tmp/gen.out.sys
	grep ^T /tmp/gen.out \| cut -f2- > /tmp/gen.out.ref
	fairseq-score --sys /tmp/gen.out.sys --ref /tmp/gen.out.ref
	# BLEU4 = 40.83, 67.5/46.9/34.4/25.5 (BP=1.000, ratio=1.006, syslen=83262, reflen=82787)
	```

	## Training a new model

	### IWSLT'14 German to English (Transformer)

	The following instructions can be used to train a Transformer model on the [IWSLT'14 German to English dataset](http://workshop2014.iwslt.org/downloads/proceeding.pdf).

	First download and preprocess the data:
	```bash
	# Download and prepare the data
	cd examples/translation/
	bash prepare-iwslt14.sh
	cd ../..

	# Preprocess/binarize the data
	TEXT=examples/translation/iwslt14.tokenized.de-en
	fairseq-preprocess --source-lang de --target-lang en \
	--trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
	--destdir data-bin/iwslt14.tokenized.de-en \
	--workers 20
	```

	Next we'll train a Transformer translation model over this data:
	```bash
	CUDA_VISIBLE_DEVICES=0 fairseq-train \
	data-bin/iwslt14.tokenized.de-en \
	--arch transformer_iwslt_de_en --share-decoder-input-output-embed \
	--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
	--lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
	--dropout 0.3 --weight-decay 0.0001 \
	--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
	--max-tokens 4096 \
	--eval-bleu \
	--eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
	--eval-bleu-detok moses \
	--eval-bleu-remove-bpe \
	--eval-bleu-print-samples \
	--best-checkpoint-metric bleu --maximize-best-checkpoint-metric
	```

	Finally we can evaluate our trained model:
	```bash
	fairseq-generate data-bin/iwslt14.tokenized.de-en \
	--path checkpoints/checkpoint_best.pt \
	--batch-size 128 --beam 5 --remove-bpe
	```

	### WMT'14 English to German (Convolutional)

	The following instructions can be used to train a Convolutional translation model on the WMT English to German dataset.
	See the [Scaling NMT README](../scaling_nmt/README.md) for instructions to train a Transformer translation model on this data.

	The WMT English to German dataset can be preprocessed using the `prepare-wmt14en2de.sh` script.
	By default it will produce a dataset that was modeled after [Attention Is All You Need (Vaswani et al., 2017)](https://arxiv.org/abs/1706.03762), but with additional news-commentary-v12 data from WMT'17.

	To use only data available in WMT'14 or to replicate results obtained in the original [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](https://arxiv.org/abs/1705.03122) paper, please use the `--icml17` option.

	```bash
	# Download and prepare the data
	cd examples/translation/
	# WMT'17 data:
	bash prepare-wmt14en2de.sh
	# or to use WMT'14 data:
	# bash prepare-wmt14en2de.sh --icml17
	cd ../..

	# Binarize the dataset
	TEXT=examples/translation/wmt17_en_de
	fairseq-preprocess \
	--source-lang en --target-lang de \
	--trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
	--destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \
	--workers 20

	# Train the model
	mkdir -p checkpoints/fconv_wmt_en_de
	fairseq-train \
	data-bin/wmt17_en_de \
	--arch fconv_wmt_en_de \
	--dropout 0.2 \
	--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
	--optimizer nag --clip-norm 0.1 \
	--lr 0.5 --lr-scheduler fixed --force-anneal 50 \
	--max-tokens 4000 \
	--save-dir checkpoints/fconv_wmt_en_de

	# Evaluate
	fairseq-generate data-bin/wmt17_en_de \
	--path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \
	--beam 5 --remove-bpe
	```

	### WMT'14 English to French
	```bash
	# Download and prepare the data
	cd examples/translation/
	bash prepare-wmt14en2fr.sh
	cd ../..

	# Binarize the dataset
	TEXT=examples/translation/wmt14_en_fr
	fairseq-preprocess \
	--source-lang en --target-lang fr \
	--trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
	--destdir data-bin/wmt14_en_fr --thresholdtgt 0 --thresholdsrc 0 \
	--workers 60

	# Train the model
	mkdir -p checkpoints/fconv_wmt_en_fr
	fairseq-train \
	data-bin/wmt14_en_fr \
	--arch fconv_wmt_en_fr \
	--dropout 0.1 \
	--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
	--optimizer nag --clip-norm 0.1 \
	--lr 0.5 --lr-scheduler fixed --force-anneal 50 \
	--max-tokens 3000 \
	--save-dir checkpoints/fconv_wmt_en_fr

	# Evaluate
	fairseq-generate \
	data-bin/fconv_wmt_en_fr \
	--path checkpoints/fconv_wmt_en_fr/checkpoint_best.pt \
	--beam 5 --remove-bpe
	```

	## Multilingual Translation

	We also support training multilingual translation models. In this example we'll
	train a multilingual `{de,fr}-en` translation model using the IWSLT'17 datasets.

	Note that we use slightly different preprocessing here than for the IWSLT'14
	En-De data above. In particular we learn a joint BPE code for all three
	languages and use fairseq-interactive and sacrebleu for scoring the test set.

	```bash
	# First install sacrebleu and sentencepiece
	pip install sacrebleu sentencepiece

	# Then download and preprocess the data
	cd examples/translation/
	bash prepare-iwslt17-multilingual.sh
	cd ../..

	# Binarize the de-en dataset
	TEXT=examples/translation/iwslt17.de_fr.en.bpe16k
	fairseq-preprocess --source-lang de --target-lang en \
	--trainpref $TEXT/train.bpe.de-en \
	--validpref $TEXT/valid0.bpe.de-en,$TEXT/valid1.bpe.de-en,$TEXT/valid2.bpe.de-en,$TEXT/valid3.bpe.de-en,$TEXT/valid4.bpe.de-en,$TEXT/valid5.bpe.de-en \
	--destdir data-bin/iwslt17.de_fr.en.bpe16k \
	--workers 10

	# Binarize the fr-en dataset
	# NOTE: it's important to reuse the en dictionary from the previous step
	fairseq-preprocess --source-lang fr --target-lang en \
	--trainpref $TEXT/train.bpe.fr-en \
	--validpref $TEXT/valid0.bpe.fr-en,$TEXT/valid1.bpe.fr-en,$TEXT/valid2.bpe.fr-en,$TEXT/valid3.bpe.fr-en,$TEXT/valid4.bpe.fr-en,$TEXT/valid5.bpe.fr-en \
	--tgtdict data-bin/iwslt17.de_fr.en.bpe16k/dict.en.txt \
	--destdir data-bin/iwslt17.de_fr.en.bpe16k \
	--workers 10

	# Train a multilingual transformer model
	# NOTE: the command below assumes 1 GPU, but accumulates gradients from
	# 8 fwd/bwd passes to simulate training on 8 GPUs
	mkdir -p checkpoints/multilingual_transformer
	CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt17.de_fr.en.bpe16k/ \
	--max-epoch 50 \
	--ddp-backend=legacy_ddp \
	--task multilingual_translation --lang-pairs de-en,fr-en \
	--arch multilingual_transformer_iwslt_de_en \
	--share-decoders --share-decoder-input-output-embed \
	--optimizer adam --adam-betas '(0.9, 0.98)' \
	--lr 0.0005 --lr-scheduler inverse_sqrt \
	--warmup-updates 4000 --warmup-init-lr '1e-07' \
	--label-smoothing 0.1 --criterion label_smoothed_cross_entropy \
	--dropout 0.3 --weight-decay 0.0001 \
	--save-dir checkpoints/multilingual_transformer \
	--max-tokens 4000 \
	--update-freq 8

	# Generate and score the test set with sacrebleu
	SRC=de
	sacrebleu --test-set iwslt17 --language-pair ${SRC}-en --echo src \
	\| python scripts/spm_encode.py --model examples/translation/iwslt17.de_fr.en.bpe16k/sentencepiece.bpe.model \
	> iwslt17.test.${SRC}-en.${SRC}.bpe
	cat iwslt17.test.${SRC}-en.${SRC}.bpe \
	\| fairseq-interactive data-bin/iwslt17.de_fr.en.bpe16k/ \
	--task multilingual_translation --lang-pairs de-en,fr-en \
	--source-lang ${SRC} --target-lang en \
	--path checkpoints/multilingual_transformer/checkpoint_best.pt \
	--buffer-size 2000 --batch-size 128 \
	--beam 5 --remove-bpe=sentencepiece \
	> iwslt17.test.${SRC}-en.en.sys
	grep ^H iwslt17.test.${SRC}-en.en.sys \| cut -f3 \
	\| sacrebleu --test-set iwslt17 --language-pair ${SRC}-en
	```

	##### Argument format during inference

	During inference it is required to specify a single `--source-lang` and
	`--target-lang`, which indicates the inference langauge direction.
	`--lang-pairs`, `--encoder-langtok`, `--decoder-langtok` have to be set to
	the same value as training.